Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression.

The functional connectivity between thalamic medio-dorsal nucleus (MD) and cortical regions, especially the dorsolateral prefrontal cortex (DLPFC), is implicated in attentional processing and is anomalous in schizophrenia, a brain disease associated with polygenic risk and attentional deficits. However, the molecular and genetic underpinnings of thalamic connectivity anomalies are unclear. Given that gene co-expression across brain areas promotes synchronous interregional activity, our aim was to investigate whether coordinated expression of genes relevant to schizophrenia in MD and DLPFC may reflect thalamic connectivity anomalies in an attention-related network including the DLPFC. With this aim, we identified in datasets of post-mortem prefrontal mRNA expression from healthy controls a gene module with robust overrepresentation of genes with coordinated MD-DLPFC expression and enriched for schizophrenia genes according to the largest genome-wide association study to date. To link this gene cluster with imaging phenotypes, we computed a Polygenic Co-Expression Index (PCI) combining single-nucleotide polymorphisms predicting module co-expression. Finally, we investigated the association between PCI and thalamic functional connectivity during attention through fMRI Independent Component Analysis in 265 healthy participants. We found that PCI was positively associated with connectivity strength of a thalamic region overlapping with the MD within an attention brain circuit. These findings identify a novel association between schizophrenia-related genes and thalamic functional connectivity. Furthermore, they highlight the association between gene expression co-regulation and brain connectivity, such that genes with coordinated MD-DLPFC expression are associated with coordinated activity between the same brain regions. We suggest that gene co-expression is a plausible mechanism underlying biological phenotypes of schizophrenia.

Topological Measurements of DWI Tractography for Alzheimer's Disease Detection.

Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer's disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%-99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.